Instructions to use RikvanSchaick/bert-finetuned-ner_trial2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RikvanSchaick/bert-finetuned-ner_trial2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RikvanSchaick/bert-finetuned-ner_trial2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RikvanSchaick/bert-finetuned-ner_trial2") model = AutoModelForTokenClassification.from_pretrained("RikvanSchaick/bert-finetuned-ner_trial2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-finetuned-ner_trial2
This model is a fine-tuned version of bert-base-cased on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 2.2216 | 0.0 | 0.0 | 0.0 | 0.7697 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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Model tree for RikvanSchaick/bert-finetuned-ner_trial2
Base model
google-bert/bert-base-cased